Triple
T7768956
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tik-Tok of Oz |
E179020
|
entity |
| Predicate | hasMainCharacter |
P1183
|
FINISHED |
| Object | Tik-Tok |
E239330
|
NE FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Tik-Tok | Statement: [Tik-Tok of Oz, hasMainCharacter, Tik-Tok]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tik-Tok Context triple: [Tik-Tok of Oz, hasMainCharacter, Tik-Tok]
-
A.
Tik-Tok
chosen
Tik-Tok is a mechanical man from L. Frank Baum’s Oz series, often considered one of the earliest robots in modern fantasy literature.
-
B.
Tikkana
Tikkana was a prominent 13th-century Telugu poet and scholar best known for translating a major portion of the Mahabharata into Telugu and helping shape classical Telugu literature.
-
C.
Mr. Scratch
Mr. Scratch is the cunning, devilish antagonist who bargains for souls in Stephen Vincent Benét’s short story "The Devil and Daniel Webster."
-
D.
Tiko
Tiko is a coastal town and port in southwestern Cameroon known for its agricultural activities and role as a transport hub.
-
E.
Esio Trot
Esio Trot is a children's novel by Roald Dahl about a shy man who uses a clever tortoise-related ruse to win the affection of his neighbor.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69c69f30602c819082ab52cd4af5c592 |
completed | March 27, 2026, 3:16 p.m. |
| NER | Named-entity recognition | batch_69c704376dc08190890f5ebb9f259cfd |
completed | March 27, 2026, 10:27 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8d6d0f1988190b4df650bebb61fd3 |
completed | March 29, 2026, 7:37 a.m. |
Created at: March 27, 2026, 4:11 p.m.